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View Code? Open in Web Editor NEWLearning multi-domain multi-modality I2I translation
Learning multi-domain multi-modality I2I translation
Hi,
I like this work very much and I think it is really cool. But when I tried to use an image in the target domain to decide the attribute of the results, I found the function βtest_forward_transfer(self, image, image_trg, c_trg)β in the file named "model.py" requires two arguments "image_trg" and "c_trg", but instead of using these two arguments, the function use "self.image_trg" and "self.c_trg", which wasn't defined in corresponding class "MD_multi()". I don't kown how to trackle this problem, so I take the liberty to ask you for help.
Thanks
Wow, a great work, I wonder there is a corresponding paper about MDMM?
Hello!
I found a following thing in LeakyReLUConv2d:
class LeakyReLUConv2d(nn.Module):
def __init__(self, ..., norm='None', ...):
....
if 'norm' == 'Instance':
model += [nn.InstanceNorm2d(n_out, affine=False)]
...
https://github.com/HsinYingLee/MDMM/blob/master/networks.py#L362
It seems that normalization is never applied in LeakyReLUConv2d block.
Does it affect the model performance, as LeakyReLUConv2d present in MultiDomain Encoder and Discriminators?
Are the best results reported in paper are gained with turned on InstanceNormalization?
Best Regards,
Aleksei Silvestrov
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